Update app.py

app.py

@@ -213,6 +213,62 @@ def predict():
         traceback.print_exc()
         return jsonify({'error': str(e)}), 500
 
+@app.route('/test-example', methods=['POST'])
+def test_example():
+    """Test with example MHA file"""
+    try:
+        example_path = 'image/VSD.Brain.XX.O.MR_Flair.35796.mha'
+        
+        if not os.path.exists(example_path):
+            return jsonify({'error': 'Example MHA file not found. Please add VSD.Brain.XX.O.MR_Flair.35796.mha to image/ folder'}), 404
+        
+        print(f"Testing with example file: {example_path}")
+        
+        # Preprocess MHA file
+        img_input, original_slice = preprocess_image(example_path)
+        
+        # Predict
+        print("Making prediction on example...")
+        prediction = model.predict(img_input, verbose=0)
+        
+        # Postprocess mask
+        mask = postprocess_mask(prediction[0], original_slice.shape)
+        
+        # Normalize original slice for display
+        original_display = ((original_slice - original_slice.min()) / 
+                           (original_slice.max() - original_slice.min() + 1e-8) * 255).astype(np.uint8)
+        
+        # Create overlay
+        overlay = create_overlay(original_display, mask)
+        
+        # Convert to base64
+        original_base64 = img_to_base64(original_display)
+        mask_base64 = img_to_base64(mask)
+        overlay_base64 = img_to_base64(overlay)
+        
+        # Calculate statistics
+        tumor_pixels = np.sum(mask > 127)
+        total_pixels = mask.shape[0] * mask.shape[1]
+        tumor_percentage = (tumor_pixels / total_pixels) * 100
+        
+        result = {
+            'original': original_base64,
+            'mask': mask_base64,
+            'overlay': overlay_base64,
+            'tumor_percentage': float(tumor_percentage),
+            'image_size': f"{mask.shape[1]}x{mask.shape[0]}"
+        }
+        
+        print(f"✓ Example prediction completed: {tumor_percentage:.2f}% tumor detected")
+        
+        return jsonify(result)
+        
+    except Exception as e:
+        print(f"Error during example prediction: {e}")
+        import traceback
+        traceback.print_exc()
+        return jsonify({'error': str(e)}), 500
+
 if __name__ == '__main__':
     print("\n" + "="*60)
     print("🧠 Brain Tumor Segmentation App")